Fine Segmentation on Faces With Masks Based on a Multistep Iterative Segmentation Algorithm

Innovation in facial recognition technology is urgent in the current epidemic situation. In order to extract more face features to perform facial recognition on images of faces with masks, we propose a multi-step iterative face-mask segmentation algorithm (MISA). The improved Mask R-CNN algorithm is used to detect the target in the face region to realize coarse segmentation, and the generalization ability and accuracy of the segmentation are improved. Then, the proposed approach uses an R-Pairwise Differential Siamese Network (R-PDSN) to train a mask occlusion classifier to subdivide the edge blocks of coarse segmentation results. The segmentation accuracy is further improved by optimizing the edge information of the masks step by step. The self-built dataset of faces with masks was used for training and testing. The experimental results showed that the mean pixel accuracy of the proposed method was improved by 2.69% compared with the original Mask R-CNN segmentation algorithm, and the target detection accuracy was more than 98%. These results indicate that the proposed method can achieve good segmentation performance on face images with complex backgrounds, self-occlusion and different types of masks. These results demonstrate that our method can improve the accuracy of segmentation methods for imaged of faces wearing masks.